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Data Analytics > Visualisation & Presentation > Flashcards

Visualisation & Presentation Flashcards

1
Q

Data Visualisation

A

The graphic representation and presentation of data.

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2
Q

5 Second Rule

A

Your audience should know what they’re looking at within the first 5 seconds of seeing it. They should also know what conclusion your visualisation is making in the 5 seconds after that.

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3
Q

The McCandless Method

A
  1. Information (data)
  2. Story (concept)
  3. Goal (function)
  4. Visual form (metaphor)
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4
Q

Kaiser Fung’s Trifecta Checkup Framework

A
  1. What is the practical question?
  2. What does the data say?
  3. What does the visual say?
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5
Q

Pre-attentive attributes

A

Pre-attentive attributes are the elements of data visualisation that people recognise automatically without conscious effort. These immediately understandable visual cues are known as marks and channels

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6
Q

Marks

A

Marks are basic visual objects like points, lines, and shapes.

Every mark can be broken down into 4 qualities:
1. Position
2. Size
3. Shape
4. Colour

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7
Q

Channels

A

Channels are visual aspects or variables that represent the characteristics of the data. Channels are basically marks that have been used to visualise data.

Channels will vary in the effectiveness of communicating data based on three elements:
1. Accuracy
2. Popout
3. Grouping

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8
Q

Design Principles (Dos)

A
  1. Choose the right visual
  2. Optimise data-ink ratio
  3. use orientation effectively
  4. Colour
  5. Number of things
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9
Q

Design Principles (Don’ts)

A
  1. Cutting off the y-axis
  2. Misleading use of a dual y-axis
  3. Artificially limiting the scope of the data
  4. Problematic choices in how data is binned or grouped
  5. Using part-to-whole visuals when the totals do not sum up appropriately
  6. Hiding trends in cumulative charts
  7. Artificially smoothing trends
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10
Q

Bar Graphs

A

Use size contrast to compare two or more values. Use size contrast to compare two or more values. The bottom bar running horizontally is the x-axis and represents categories, time periods or other variables in bar graphs with vertical bars. The vertical line of bar graphs usually placed to the left is the y-axis and has the scale of values for the variables.

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11
Q

Line Graphs

A

Help your audience understand shifts or changes in your data.

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12
Q

Pie Charts

A

Show how much each part of something makes up the whole.

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13
Q

Maps

A

Help organise data geographically.

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14
Q

Histogram

A

A chart that shows how often data values fall into certain ranges.

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15
Q

Correlation Charts

A

Show relationships among data.

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16
Q

Causation

A

Occurs when an action directly leads to an outcome.

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17
Q

Correlation

A

Correlation in statistics is the measure of the degree to which two variables move in relation to each other. Correlation doesn’t mean that one event caused another. But, it does indicate that they have a pattern with or a relationship to each other.

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18
Q

Types of Correlation

A

Positive Correlation - one variable goes up causing the other to go up also.

Negative (Inverse) Correlation - one variable goes up and the other goes down.

No Correlation - One variable goes up whilst the other variable remains the same.

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19
Q

Dynamic Visualisations

A

Visualisations that are interactive or change over time. User have some control over what they see, which can be useful for stakeholders.

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20
Q

Static Visualisations

A

Do not change over time unless they’re edited. They’re useful when controlling data or the story. Any printed visualisations are automatically static.

21
Q

Tableau

A

A business intelligence and analytics platform that helps people see, understand, and make decisions with data. Visualisations on this platform are automatically interactive.

22
Q

Change

A

This is a trend or instance of observations that become different over time. A great way to measure change in data is through a line or column chart.

23
Q

Clustering

A

A collection of data points with similar or different values. This is best represented through a distribution graph.

24
Q

Relativity

A

These are observations considered in relation or in proportion to something else. You have probably seen examples of relativity data in a pie chart.

25
Q

Ranking

A

This is a position in a scale of achievement or status. Data that requires ranking is best represented by a column chart.

26
Q

Correlation

A

This shows a mutual relationship or connection between two or more things. A scatterplot is an excellent way to represent this type of data pattern.

27
Q

Decision Tree

A

A decision making tool that allows you, the data analyst, to make decisions based on key questions that you can ask yourself.

28
Q

Elements of art

A
  • Line - curved or straight, thick or thin, vertical, horizontal or diagonal. They add visual form to your data and help build the structure for your visualisation.
  • Shape - always two-dimensional as 3D shapes in visualisation can complicate the visual and confuse the audience. Shapes with symmetry are usually more familiar to people.
  • Colour - Think hue, intensity, and value. The hue is the colour. The intensity is how bright or dull the colour is. The value defines how much light is being reflected or the colour’s lightness or darkness.
  • Space - the area between, around, and in the object(s).

Movement - used to create a sense of flow or action in a visualisation.

29
Q

Nine basic principles of design

A
  1. Balance - the design of a data visualisation is balanced when the key visual elements, like colour and shape, are distributed evenly.
  2. Emphasis - your data visualisation should have a focal point so that your audience knows where to concentrate.
  3. Movement - this can refer to the path the viewer’s eye travels as they look at a data visualisation or literal movement created by animations. Movement in data visualisation should mimic the way people usually read.
  4. Pattern - you can use similar shapes and colours to create patterns in your visualisation.
  5. Repetition - repeating chart types, shapes, or colours adds to the effectiveness of your visualisation.
  6. Proportion - this is another way that you can demonstrate the importance of certain data
  7. Rhythm - this refers to creating a sense of movement or flow in your visualisation.
  8. Variety - your visualisation should have some variety in the chart types, lines, shapes, colours, and values you use.
  9. Unity - this means that your final data visualisation should be cohesive.
30
Q

Data composition

A

Combining the individual parts in a visualisation and displaying them together as a whole.

31
Q

Elements for effective visuals

A
  • Clear meaning - clearly communicating intended insight.
  • Sophisticated use of contrast - separation of the most important data from the rest using visual context that our brain naturally looks for.
  • Refined execution - use of deep attention to detail using visual elements like lines, shapes, colours, values, space, and movement. In other words the elements of art.
32
Q

Design thinking

A

A process used to solve complex problems in a user-centric way.

33
Q

Five phases of the design process

A
  • Empathise - think about the emotions and needs of the target audience of your data viz.
  • Define - define your audience’s needs, their problems, and your insights.
  • Ideate - you start to generate your data viz ideas and brainstorm potential data viz solutions.
  • Prototype - start putting your charts, dashboards and other visualisations together.
  • Test - you can test your visualisations by showing them to team members before presenting them to stakeholders. Listen to any feedback you get.
34
Q

Headline

A

A line of words printed in large letters at the top of the visualisation to communicate what data is being presented.

35
Q

Subtitles

A

Supports the headline by adding more context and description.

36
Q

Labels

A

Use labels directly on the data as opposed to relying on the legends.

37
Q

Legend

A

Identifies the meaning of various elements in a data visualisation.

38
Q

Ways to make data visualisations more accessible

A
  • Labeling (try labelling data directly)
  • Text alternatives (braille)
  • Text-based format
  • Distinguishing (use different textures and avoid relying on colour)
  • Simplify
39
Q

Colour Blindness

A

Red-green colour blindness is the most common and occurs when red and green look like the same colour. You can avoid placing green on red or red on green in your visualizations.

Blue-yellow colour blindness is less common and occurs when it is difficult to tell the difference between blue and green, or yellow and red. You can also avoid using these colours on top of or next to each other.

40
Q

Data Visualisations Tools

A

Tableau - browser, desktop
Looker - browser-only
Google Data Studio - browser-only

41
Q

Diverging colour palette

A

Displays two ranges of values using colour intensity to show the magnitude of the number and the actual colour to show which range the number is from.

42
Q

Data Storytelling

A

Communicating the meaning of a dataset with visuals and a narrative that are customised for each particular audience.

43
Q

3 data storytelling steps

A
  1. Engage your audience
  2. Create compelling visuals
  3. Tell the story in an interesting way
44
Q

Engagement

A

Engagement is capturing and holding someone’s interest and attention.

Ask:
- What role does this audience play?
- What is their stake in the project?
- What do they hope to get from the data insights I deliver?

45
Q

Spotlighting

A

Scanning through the data to quickly identify the most important insights.

Post-it notes of each insight spread out on a whiteboard is a great way of spotlighting.

After spotlighting examine the insights.

46
Q

Tableau dashboard basics

A
  • Dashboards constantly monitor live incoming data.
  • When designing a dashboard start simple with the most important data points.
  • The placement or layout of charts, graphs and other visuals is important. These elements need to be cohesive and make good use of the space available on the dashboard.
47
Q

Effective storytelling

A

An effective data narrative includes characters, a setting, a plot, a big reveal, and an aha moment. Describe the difference between the big reveal and the aha moment.

The big reveal involves how the data has shown that the problem can be solved. The aha moment is when recommendations are shared.

48
Q
A

Data Analytics (10 decks)

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